Fuzzy Clustering Algorithm Research Articles

A Precise sensing method of campus network security situation based on fuzzy clustering algorithm

A Precise sensing method of campus network security situation based on fuzzy clustering algorithm

Assessment of the State of Plant Biomass Based on the Integration of Multispectral Sensors of Optical and Radio Ranges

One of the main tasks of using remote sensing in agriculture for precision farming purposes is to identify management zones or management zones within which the timing and parameters of agrotechnical measures differ significantly. To clarify the boundaries of these zones, it is proposed to use jointly data on soil moisture (electrical conductivity) and the normalized plant index (NDVI) in a field of about 70 hectares. Based on spatial variations of humidity data obtained using a bistatic radar system and electrical conductivity obtained using electromagnetic scanning, as well as NDVI indices obtained using multispectral cameras, maps of the spatial distribution of these parameters are constructed. To determine the control zones, a fuzzy clustering algorithm was used, three target classes for assessing the state of plant biomass with restrictions on the percentage of moisture in the soil were identified. An analysis of 813 points of the soil surface was carried out with reference to geographical coordinates, the elements of the array were assigned to one of the target classes corresponding to one of the three control zones. The results of the analysis of arrays formed by classes allow us to conclude that it is possible to use fuzzy clustering to determine the boundaries of control zones in conditions of significant heterogeneity of the studied fields in terms of physico-chemical properties and relief.

The Impact of Big Data Analytics Capabilities on the Diversification of E-Commerce Firms

Abstract With the arrival of the big data era, e-commerce enterprises are facing more and more competition and market changes. A fuzzy clustering model is created by combining fuzzy set theory and fuzzy logic with a clustering algorithm in this paper. Then, for the shortcomings of the fuzzy C-mean clustering algorithm, the genetic simulated annealing algorithm is used to weight the data points, the fuzzy C-mean clustering model is improved, and a fuzzy clustering algorithm based on the genetic simulated annealing optimization is proposed. By using this algorithm, e-commerce enterprises’ diversification is being analyzed. The results show that the mean and standard deviation of each supply chain inventory, production orders, supplier ordering, and platform ordering quantity after optimization are reduced to different degrees, some of which reach more than 60%. Stabilizing the development of e-commerce enterprises can be achieved by reducing supply chain inventory and order quantity. E-commerce enterprises that excel in their enterprise production quality (0.741), credibility (0.748), and product sales competitiveness (0.726) are more competitive. E-commerce enterprises can utilize the ability of big data analysis to achieve diversification.

Practical Research on the Reform of Social Practice Teaching in Colleges and Universities under the Background of Big Data

Abstract Social practice teaching in colleges and universities combines theory and practice, transforms professional theory into practical behavior, and innovates students’ way of thinking, which can promote the teaching of colleges and universities as well as the cultivation of target talents. The big data era is innovating the traditional social practice teaching mode, and new opportunities are available for the development of social practice teaching. By analyzing the development form of social practice in colleges and universities in the era of big data, we propose to use the fuzzy C-mean clustering algorithm to deal with students’ social practice behaviors, to classify the type of students’ participation in social practice in colleges and universities, and to grasp the development status of the reform of social practice teaching in colleges and universities in the era of big data. The rating of dimensions of the social practice process in colleges and universities is used to determine the overall development level of social practice reform in colleges and universities. It also analyzes the differences in the process of carrying out social practice in colleges and universities, with the types of colleges and universities and categories of specialties as independent variables. When K is equal to 4, the contour coefficient is the largest, and the clustering effect is the best, so the students’ social practice participation can be reflected in four types: “need to improve”, “backward”, “hard-working” and “active”, combined with the overall score of social practice teaching reform in colleges and universities in the era of big data is 3.81±0.82 points, and the scores of all dimensions are above 3.75, and the overall development of social practice teaching reform in colleges and universities proposed in this paper is better.

An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare.

Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able to work for multi-modal data. This paper presents a new high-order multi-modal learning approach using multimodal deep learning and the Tucker decomposition (F- HoFCM). Furthermore, we propose an edge-cloud-aided private scheme to facilitate the clustering efficiency for its embedding in edge resources. Specifically, the computationally intensive tasks, such as parameter updating with high-order back propagation algorithm and clustering through high-order fuzzy c-means, are processed in a centralized location with cloud computing. The other tasks such as multi-modal data fusion and Tucker decomposition are performed at the edge resources. Since the feature fusion and Tucker decomposition are nonlinear operations, the cloud cannot obtain the raw data, thus protecting the privacy. Experimental results state that the presented approach produces significantly more accurate results than the existing high-order fuzzy c-means (HOFCM) on multi-modal healthcare datasets and furthermore the clustering efficiency are significantly improved by the developed edge-cloud-aided private healthcare system.

Prioritizing solutions to mitigate the risks due to adoption of intermodal railroad freight transportation for achieving sustainable development goals

Multiple cleaner strategies are being devised by governments worldwide to incorporate approaches aligning with sustainable development goals (SDGs), placing particular emphasis on the modal shift from unimodal road to intermodal railroad (IRR) freight transportation. This shift is gaining traction due to its documented positive impact on environmental, economic, and social sustainability. However, adopting IRR freight transportation in emerging economies faces hurdles, primarily attributable to inherent modal shift risks (MSRs). Addressing these MSRs requires the implementation of modal shift solutions (MSSs). This study aims to prioritize the MSSs to mitigate the impact of MSRs in IRR adoption, focusing on achieving SDGs with a case study conducted in the Indian logistics sector. The study is structured into four phases. The first phase identifies 37 sub-MSRs, categorized into seven main MSRs, accompanied by the 39 MSSs for IRR freight transportation adoption. The second phase employs the spherical fuzzy Bayesian best-worst method (SF-BBWM) to assess the relative weights of main and sub-MSRs. The third phase uses the spherical fuzzy C-means clustering (SF-CMC) algorithm to identify crucial MSSs. Finally, the fourth phase utilizes the spherical fuzzy combinative distance-based assessment (SF-CODAS) to prioritize these crucial MSSs. From the identified MSSs, pivotal solutions include developing an integrated physical IRR network across the freight transport chain and fostering relationships between stakeholders through long-term contracts and strategic partnerships. The available results are discussed to facilitate logistics stakeholders’ understanding of the implementation of MSSs aligning with SDGs to mitigate the impact of MSRs.

Exploring the development of athletic interests of students in physical education colleges and universities based on cluster analysis: physical education athletic training

Abstract Sports interest is the internal motivation for students in physical education colleges and universities to participate in sports training enthusiastically. Based on the definition of fuzzy set and fuzzy clustering uncertainty measure, this paper utilizes the fuzzy C-mean clustering algorithm to evaluate and analyze students’ athletic training performance data by clustering, and based on the results of the analysis, it formulates the physical education teaching program based on the development of students’ athletic interest. At the same time, taking the physical education course of grade 2022 in College Q as an example, the IPA quadrant diagram was used to analyze the ‘importance-satisfaction’ of physical education teaching. The results showed that in the IPA quadrant, the five factors of teachers’ professional quality [4.89,4.13], teachers’ ability to explain [4.56,4.16], mastery of movement [4.64,4.03], novel content of teaching materials [4.46,3.99] and good image of the teacher [4.42,4.19] were in the region of high importance-satisfaction, which indicated that the continuation of these factors could improve students’ satisfaction of physical education teaching. The role can increase students’ interest in sports and overall satisfaction with physical education sports training. From the side, it verifies the feasibility of the teaching strategy proposed in this paper. It provides data support for the in-depth implementation of quality education and the cultivation of moral, intellectual, physical, and aesthetic talents of all-round development.

Exploration and Practice of English Civics Teaching Design in Colleges and Universities Based on Big Data Analysis

Abstract This study explored the cutting-edge methodology of integrating curricular Civics into teaching specialized disciplines in higher education, an essential trend for the future development of higher education. By applying data-mining algorithms, the article constructs a big-data analysis framework from the dimensions of students’ classroom behavior and performance. The fuzzy C-mean clustering algorithm was optimized using the cuckoo algorithm to effectively classify the characteristics of students’ behavior in the Civics classroom, and the attention-based hybrid encoder-decoder model was used to accurately predict students’ grades. After establishing a complete analytical framework, an innovative college English Civics teaching model was developed following the ASSURE teaching model. It was applied and analyzed for its effect in actual teaching. The study results show that, after the implementation of the English Civics and Politics course based on the ASSURE model, the average grade of the students increased by about 0.5 points. The grades were distributed between 80 and 95 points, significantly different from the pre-test grades (P<0.05). The prediction error was controlled to be less than ±2 points, which indicates that the teaching model had a significant teaching effect. This study not only improves English learning performance but also promotes the improvement of students’ performance in Civics, showing the great potential and value of integrating Civics curriculum into professional teaching.

Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

Development of Fuzzy C-Means with Fuzzy Chebyshev for genomic clustering solutions addressing cancer issues

Clustering is a crucial technique used to identify the structure of data sets. It involves the task of splitting groups into different clusters based on suitable similarity measures. Fuzzy C-Means is a clustering technique that extends the fuzzy set theory. It is a widely used method among existing fuzzy clustering algorithms due to its easy implementation and straightforward approach. However, it still has some drawbacks, such as high sensitivity to outliers or noisy data, as well as to initialization conditions. Therefore, this paper proposes a modification to the Fuzzy C-Means algorithm using Fuzzy Chebyshev. The preprocessing of raw datasets includes the use of MinMax Scaler. Additionally, dimensional reduction techniques are applied to effectively address issues related to high-dimensional data. To determine the optimal number of clusters in the data set, the Elbow method is implemented. The final clustering is achieved by employing Fuzzy Chebyshev to replace the distance measurement component in the Fuzzy C-Means algorithm. A comparison between the proposed method and previous methods is performed. The effectiveness of the proposed method is illustrated through a numerical example involving genomic data of prostate cancer. Comparable results are presented to validate the feasibility of the proposed method. The results demonstrate that the proposed method aligns with those obtained from previous methods.

INTUITIONISTIC ROBUST CLUSTERING FOR SEGMENTATION OF LESIONS IN DERMATOSCOPIC IMAGES

This paper presents the formulation of the intuitive fuzzy clustering algorithm to be robust to atypical data present in dermoscopic images and to delimit the affected area. This algorithm is formulated from the objective function derivation for memberships update, to integrate an m-redescending estimator influence function. Experimentation shows an accuracy of 95% with the proposal algorithm with respect to other clustering algorithms to perform delimitations, in addition the iterations number is considerably reduced. Keywords: Robust Intuitionistic Fuzzy Clustering, Dermoscopic Images, Delimitations of Lesions, M-redescending Estimator

Novel multiple criteria group decision-making method based on hesitant fuzzy clustering algorithm

Novel multiple criteria group decision-making method based on hesitant fuzzy clustering algorithm

Digital Technology-Driven Transformation of Teaching Model of Digital Animation Major and Enhancement of Students’ Artistic Literacy

Abstract Digital technology, at the center of the fourth technological revolution, is currently transforming all aspects of people’s lives and learning in an unprecedented manner. In light of the ongoing advancements in education technology, this study introduces an enhanced C-ADDIE teaching model, which is competence-oriented and builds on the foundation of the traditional ADDIE model while also catering to the needs of students majoring in digital animation. The enhanced fuzzy clustering algorithm establishes a portrait of the student’s behavior, followed by the introduction of a multi-attention mechanism that builds a digital professional student performance prediction model and a supplementary teaching system. We are developing a teaching model that integrates digital technology with the C-ADDIE model. The testing of the teaching system reveals that the grade prediction accuracy ranges from 88% to 98%, with an average prediction accuracy of 93.02%. The average response time is 1.5ms, the CPU usage remains stable between 5% and 23%, and the overall performance of the teaching system is good. The results of the teaching experiment show that the average grade of the teaching mode of this paper is 5.28 points higher than the average grade of the traditional teaching mode, and at the same time, it also has a significant effect on the improvement of knowledge cultivation, artistic expression and creativity, and artistic aesthetic cultivation of artistic literacy (P<0.05). This study’s results contribute to the transformation of teaching in digital animation and offer a new research idea to enhance students’ artistic literacy.

Shaanxi Red Cultural Resources into Party Building Education in Colleges and Universities under New Media Environment

Abstract Under the new media environment, exploring how Shaanxi red cultural resources can be integrated into party building education in colleges and universities significantly improves students’ political literacy and cultural cognition. The study adopts new media technology, combines K-means clustering algorithm and fuzzy cluster analysis method to extract students’ personalized characteristics, and uses them to optimize the content of party building education. The experimental data show that the intra-class average error of the K-means algorithm in the classification of excellent students is 2.56, while the overall average intra-class mistake of the fuzzy clustering algorithm is 10.28, indicating that the latter is more scientific and accurate in party assessment. Through the research and analysis of the student groups in 30 universities, it was found that 80% of the students believed that the universities paid attention to the development of intelligent party building, and 0.9198 of the students thought that the red cultural resources in Shaanxi could meet the needs of party building education. The study’s conclusion shows that combining technical means in the new media environment and Shaanxi red cultural resources can effectively improve the effect of party building education work in colleges and universities, and enhance students’ political literacy and cultural cognition.

Interclass Balance Factor-Based Membership Fusion Semi-Supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

Interclass Balance Factor-Based Membership Fusion Semi-Supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

СРАВНЕНИЕ АЛГОРИТМОВ ГУСТАФСОНА‒КЕССЕЛЯ И C-SHELL ДЛЯ НЕЧЕТКОЙ КЛАСТЕРИЗАЦИИ КОМПЛЕКСНОЗНАЧНЫХ ДАННЫХ

The article discusses the Gustafson‒Kessel and C-Shell fuzzy clustering algorithms for complex-valued data, presents the study results using graphs and makes a conclusion about the work done.

Dynamic fuzzy clustering algorithm using cumulative cost curve with application to dynamic customer segmentation

Dynamic fuzzy clustering algorithm using cumulative cost curve with application to dynamic customer segmentation

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

Linear Diophantine Fuzzy Clustering Algorithm Based on Correlation Coefficient and Analysis on Logistic Efficiency of Food Products

Retracted: Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

Retracted: Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.